Andrew Saydjari
Andrew Saydjari is a NASA Hubble Fellow in the Department of Astrophysical Sciences at Princeton. Saydjari’s research focuses on combining astrophysics, statistics, and high-performance coding to study the chemical, spatial, and kinematic variations in the dust that permeates the Milky Way. This involves developing Bayesian methods and data reduction pipelines for spectroscopic and imaging surveys containing millions and billions of stars, respectively, usually implemented in Julia.
Session
As we move deeper into the “big data astronomy” era, the need for fast, stable, homogenous data reduction pipelines is more pressing. I will present the recent development of a pure Julia pipeline for the APOGEE instrument that takes 3D non-destructive readout images to 1D wavelength calibrated stellar spectra components. I will emphasize implementations of new/old methods of general interest to the JuliaAstro community and the desiderata to facilitate both daily and large HPC reductions.